FastJAM: a Fast Joint Alignment Model for Images

Omri Hirsch, Ron Shapira Weber, Shira Ifergane, Oren Freifeld

Keywords: Computer vision, graph neural network, GNN, image congealing, joint alignment

TL;DR: Joint alignment of multiple images without a reference in 50 seconds or less

Abstract:

Joint Alignment (JA) of images aims to align a collection of images into a unified coordinate frame, such that semantically-similar features appear at corresponding spatial locations. Most existing approaches often require long training times, large-capacity models,
and extensive hyperparameter tuning. We introduce FastJAM, a rapid, graph-based method that drastically reduces the computational complexity of joint alignment tasks. FastJAM leverages pairwise matches computed by an off-the-shelf image matcher, together with a rapid nonparametric clustering, to construct a graph representing intra- and inter-image keypoint relations.
A graph neural network propagates and aggregates these correspondences, efficiently predicting per-image homography parameters via image-level pooling. Utilizing an inverse-compositional loss, that eliminates the need
for a regularization term over the predicted transformations (and thus
also obviates the hyperparameter tuning associated with such terms), FastJAM performs image JA quickly and effectively. Experimental results on several benchmarks demonstrate that FastJAM achieves results better than existing modern JA methods in terms of alignment quality, while reducing computation time from hours or minutes to mere seconds. Our code is available at our project webpage, https://bgu-cs-vil.github.io/FastJAM/.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 5063